Overview

Dataset statistics

Number of variables12
Number of observations681
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)0.1%
Total size in memory176.4 KiB
Average record size in memory265.2 B

Variable types

Categorical2
Numeric10

Alerts

Dataset has 1 (0.1%) duplicate rowsDuplicates
Nombre has a high cardinality: 233 distinct valuesHigh cardinality
Acustica is highly overall correlated with Energía and 1 other fieldsHigh correlation
Energía is highly overall correlated with Acustica and 2 other fieldsHigh correlation
Sonoridad is highly overall correlated with Acustica and 1 other fieldsHigh correlation
Valencia is highly overall correlated with EnergíaHigh correlation
Instrumentalidad has 264 (38.8%) zerosZeros

Reproduction

Analysis started2023-04-07 19:34:20.620645
Analysis finished2023-04-07 19:34:56.761470
Duration36.14 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Nombre
Categorical

Distinct233
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Memory size58.5 KiB
Welcome To New York
 
7
This Love
 
7
I Know Places
 
7
How You Get The Girl
 
7
All You Had To Do Was Stay
 
7
Other values (228)
646 

Length

Max length84
Median length56
Mean length17.259912
Min length2

Characters and Unicode

Total characters11754
Distinct characters64
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)9.0%

Sample

1st rowAnti-Hero
2nd rowLavender Haze
3rd rowDon’t Blame Me
4th rowMidnight Rain
5th rowcardigan

Common Values

ValueCountFrequency (%)
Welcome To New York 7
 
1.0%
This Love 7
 
1.0%
I Know Places 7
 
1.0%
How You Get The Girl 7
 
1.0%
All You Had To Do Was Stay 7
 
1.0%
I Wish You Would 7
 
1.0%
Clean 7
 
1.0%
Blank Space 7
 
1.0%
Shake It Off 7
 
1.0%
Style 7
 
1.0%
Other values (223) 611
89.7%

Length

2023-04-07T13:34:57.074729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 117
 
5.5%
version 91
 
4.3%
you 58
 
2.7%
taylor's 48
 
2.3%
i 41
 
1.9%
feat 38
 
1.8%
taylor’s 36
 
1.7%
me 34
 
1.6%
29
 
1.4%
to 25
 
1.2%
Other values (363) 1605
75.6%

Most occurring characters

ValueCountFrequency (%)
1441
 
12.3%
e 1159
 
9.9%
o 808
 
6.9%
a 693
 
5.9%
r 687
 
5.8%
t 535
 
4.6%
n 522
 
4.4%
i 499
 
4.2%
s 479
 
4.1%
l 454
 
3.9%
Other values (54) 4477
38.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7967
67.8%
Uppercase Letter 1774
 
15.1%
Space Separator 1441
 
12.3%
Other Punctuation 181
 
1.5%
Open Punctuation 151
 
1.3%
Close Punctuation 151
 
1.3%
Final Punctuation 45
 
0.4%
Dash Punctuation 21
 
0.2%
Decimal Number 18
 
0.2%
Initial Punctuation 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1159
14.5%
o 808
10.1%
a 693
 
8.7%
r 687
 
8.6%
t 535
 
6.7%
n 522
 
6.6%
i 499
 
6.3%
s 479
 
6.0%
l 454
 
5.7%
h 359
 
4.5%
Other values (15) 1772
22.2%
Uppercase Letter
ValueCountFrequency (%)
T 288
16.2%
S 149
 
8.4%
W 122
 
6.9%
V 121
 
6.8%
B 117
 
6.6%
I 91
 
5.1%
M 86
 
4.8%
Y 80
 
4.5%
O 79
 
4.5%
A 75
 
4.2%
Other values (14) 566
31.9%
Other Punctuation
ValueCountFrequency (%)
' 77
42.5%
. 72
39.8%
, 13
 
7.2%
& 11
 
6.1%
? 6
 
3.3%
! 2
 
1.1%
Decimal Number
ValueCountFrequency (%)
2 10
55.6%
1 6
33.3%
0 2
 
11.1%
Space Separator
ValueCountFrequency (%)
1441
100.0%
Open Punctuation
ValueCountFrequency (%)
( 151
100.0%
Close Punctuation
ValueCountFrequency (%)
) 151
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 45
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9741
82.9%
Common 2013
 
17.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1159
 
11.9%
o 808
 
8.3%
a 693
 
7.1%
r 687
 
7.1%
t 535
 
5.5%
n 522
 
5.4%
i 499
 
5.1%
s 479
 
4.9%
l 454
 
4.7%
h 359
 
3.7%
Other values (39) 3546
36.4%
Common
ValueCountFrequency (%)
1441
71.6%
( 151
 
7.5%
) 151
 
7.5%
' 77
 
3.8%
. 72
 
3.6%
’ 45
 
2.2%
- 21
 
1.0%
, 13
 
0.6%
& 11
 
0.5%
2 10
 
0.5%
Other values (5) 21
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11704
99.6%
Punctuation 50
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1441
 
12.3%
e 1159
 
9.9%
o 808
 
6.9%
a 693
 
5.9%
r 687
 
5.9%
t 535
 
4.6%
n 522
 
4.5%
i 499
 
4.3%
s 479
 
4.1%
l 454
 
3.9%
Other values (52) 4427
37.8%
Punctuation
ValueCountFrequency (%)
’ 45
90.0%
‘ 5
 
10.0%

Album
Categorical

Distinct22
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size53.8 KiB
Fearless
61 
Red (Taylor's Version)
58 
evermore
45 
Red (Deluxe Edition)
 
42
Midnights (3am Edition)
 
40
Other values (17)
435 

Length

Max length27
Median length22
Mean length15.933921
Min length3

Characters and Unicode

Total characters10851
Distinct characters41
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMidnights
2nd rowMidnights
3rd rowreputation
4th rowMidnights
5th rowfolklore

Common Values

ValueCountFrequency (%)
Fearless 61
 
9.0%
Red (Taylor's Version) 58
 
8.5%
evermore 45
 
6.6%
Red (Deluxe Edition) 42
 
6.2%
Midnights (3am Edition) 40
 
5.9%
1989 39
 
5.7%
evermore (deluxe version) 34
 
5.0%
folklore (deluxe version) 34
 
5.0%
folklore 32
 
4.7%
1989 (Deluxe) 32
 
4.7%
Other values (12) 264
38.8%

Length

2023-04-07T13:34:57.369380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
deluxe 211
14.2%
edition 169
11.3%
version 152
10.2%
fearless 125
 
8.4%
red 116
 
7.8%
1989 102
 
6.9%
taylor's 84
 
5.6%
evermore 79
 
5.3%
midnights 66
 
4.4%
folklore 66
 
4.4%
Other values (9) 319
21.4%

Most occurring characters

ValueCountFrequency (%)
e 1362
 
12.6%
808
 
7.4%
o 743
 
6.8%
i 703
 
6.5%
r 646
 
6.0%
l 618
 
5.7%
s 552
 
5.1%
n 440
 
4.1%
a 436
 
4.0%
d 419
 
3.9%
Other values (31) 4124
38.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7752
71.4%
Uppercase Letter 1051
 
9.7%
Space Separator 808
 
7.4%
Decimal Number 448
 
4.1%
Close Punctuation 354
 
3.3%
Open Punctuation 354
 
3.3%
Other Punctuation 84
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1362
17.6%
o 743
9.6%
i 703
9.1%
r 646
8.3%
l 618
8.0%
s 552
 
7.1%
n 440
 
5.7%
a 436
 
5.6%
d 419
 
5.4%
t 331
 
4.3%
Other values (12) 1502
19.4%
Uppercase Letter
ValueCountFrequency (%)
E 169
16.1%
D 143
13.6%
F 125
11.9%
R 116
11.0%
T 112
10.7%
S 94
8.9%
V 84
8.0%
N 66
 
6.3%
M 66
 
6.3%
P 58
 
5.5%
Decimal Number
ValueCountFrequency (%)
9 204
45.5%
8 102
22.8%
1 102
22.8%
3 40
 
8.9%
Space Separator
ValueCountFrequency (%)
808
100.0%
Close Punctuation
ValueCountFrequency (%)
) 354
100.0%
Open Punctuation
ValueCountFrequency (%)
( 354
100.0%
Other Punctuation
ValueCountFrequency (%)
' 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8803
81.1%
Common 2048
 
18.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1362
15.5%
o 743
 
8.4%
i 703
 
8.0%
r 646
 
7.3%
l 618
 
7.0%
s 552
 
6.3%
n 440
 
5.0%
a 436
 
5.0%
d 419
 
4.8%
t 331
 
3.8%
Other values (23) 2553
29.0%
Common
ValueCountFrequency (%)
808
39.5%
) 354
17.3%
( 354
17.3%
9 204
 
10.0%
8 102
 
5.0%
1 102
 
5.0%
' 84
 
4.1%
3 40
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10851
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1362
 
12.6%
808
 
7.4%
o 743
 
6.8%
i 703
 
6.5%
r 646
 
6.0%
l 618
 
5.7%
s 552
 
5.1%
n 440
 
4.1%
a 436
 
4.0%
d 419
 
3.9%
Other values (31) 4124
38.0%

Acustica
Real number (ℝ)

Distinct353
Distinct (%)51.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31304133
Minimum0.000191
Maximum0.971
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-04-07T13:34:57.708362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.000191
5-th percentile0.00245
Q10.0371
median0.162
Q30.637
95-th percentile0.878
Maximum0.971
Range0.970809
Interquartile range (IQR)0.5999

Descriptive statistics

Standard deviation0.32479651
Coefficient of variation (CV)1.0375516
Kurtosis-1.1250239
Mean0.31304133
Median Absolute Deviation (MAD)0.15271
Skewness0.69631297
Sum213.18114
Variance0.10549278
MonotonicityNot monotonic
2023-04-07T13:34:58.050770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.878 7
 
1.0%
0.819 6
 
0.9%
0.13 5
 
0.7%
0.92 5
 
0.7%
0.835 5
 
0.7%
0.0158 5
 
0.7%
0.0871 5
 
0.7%
0.101 5
 
0.7%
0.937 5
 
0.7%
0.00463 5
 
0.7%
Other values (343) 628
92.2%
ValueCountFrequency (%)
0.000191 1
 
0.1%
0.000197 3
0.4%
0.000315 2
0.3%
0.000328 2
0.3%
0.000443 2
0.3%
0.000743 3
0.4%
0.00103 4
0.6%
0.00108 2
0.3%
0.00115 2
0.3%
0.00188 2
0.3%
ValueCountFrequency (%)
0.971 1
 
0.1%
0.967 2
 
0.3%
0.966 4
0.6%
0.964 2
 
0.3%
0.937 5
0.7%
0.921 1
 
0.1%
0.92 5
0.7%
0.918 3
0.4%
0.916 1
 
0.1%
0.907 1
 
0.1%

Bailabilida
Real number (ℝ)

Distinct251
Distinct (%)36.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58687225
Minimum0.175
Maximum0.897
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-04-07T13:34:58.898013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.175
5-th percentile0.392
Q10.519
median0.596
Q30.651
95-th percentile0.771
Maximum0.897
Range0.722
Interquartile range (IQR)0.132

Descriptive statistics

Standard deviation0.11077364
Coefficient of variation (CV)0.18875257
Kurtosis0.30275123
Mean0.58687225
Median Absolute Deviation (MAD)0.062
Skewness-0.16499946
Sum399.66
Variance0.0122708
MonotonicityNot monotonic
2023-04-07T13:34:59.609385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.602 14
 
2.1%
0.605 12
 
1.8%
0.553 10
 
1.5%
0.624 10
 
1.5%
0.559 10
 
1.5%
0.546 10
 
1.5%
0.598 9
 
1.3%
0.622 8
 
1.2%
0.596 7
 
1.0%
0.588 7
 
1.0%
Other values (241) 584
85.8%
ValueCountFrequency (%)
0.175 1
 
0.1%
0.292 1
 
0.1%
0.298 3
0.4%
0.31 1
 
0.1%
0.313 1
 
0.1%
0.316 3
0.4%
0.317 1
 
0.1%
0.329 2
0.3%
0.334 1
 
0.1%
0.336 1
 
0.1%
ValueCountFrequency (%)
0.897 1
 
0.1%
0.875 1
 
0.1%
0.87 2
0.3%
0.843 3
0.4%
0.842 1
 
0.1%
0.841 2
0.3%
0.828 2
0.3%
0.824 1
 
0.1%
0.815 3
0.4%
0.811 1
 
0.1%

Energía
Real number (ℝ)

Distinct313
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58515565
Minimum0.151
Maximum0.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-04-07T13:35:00.261945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.151
5-th percentile0.27
Q10.458
median0.606
Q30.733
95-th percentile0.854
Maximum0.95
Range0.799
Interquartile range (IQR)0.275

Descriptive statistics

Standard deviation0.18274752
Coefficient of variation (CV)0.31230582
Kurtosis-0.77600231
Mean0.58515565
Median Absolute Deviation (MAD)0.141
Skewness-0.22433064
Sum398.491
Variance0.033396655
MonotonicityNot monotonic
2023-04-07T13:35:00.650418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.634 10
 
1.5%
0.488 10
 
1.5%
0.777 9
 
1.3%
0.377 7
 
1.0%
0.459 7
 
1.0%
0.736 6
 
0.9%
0.785 6
 
0.9%
0.38 6
 
0.9%
0.791 5
 
0.7%
0.841 5
 
0.7%
Other values (303) 610
89.6%
ValueCountFrequency (%)
0.151 1
 
0.1%
0.156 1
 
0.1%
0.159 1
 
0.1%
0.16 2
0.3%
0.161 1
 
0.1%
0.163 1
 
0.1%
0.175 1
 
0.1%
0.178 1
 
0.1%
0.181 3
0.4%
0.182 1
 
0.1%
ValueCountFrequency (%)
0.95 2
0.3%
0.944 2
0.3%
0.935 1
 
0.1%
0.934 2
0.3%
0.917 4
0.6%
0.909 1
 
0.1%
0.902 2
0.3%
0.898 4
0.6%
0.896 3
0.4%
0.893 3
0.4%

Instrumentalidad
Real number (ℝ)

Distinct240
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0051035166
Minimum0
Maximum0.488
Zeros264
Zeros (%)38.8%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-04-07T13:35:01.262094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.15 × 10-6
Q37.59 × 10-5
95-th percentile0.00322
Maximum0.488
Range0.488
Interquartile range (IQR)7.59 × 10-5

Descriptive statistics

Standard deviation0.036005696
Coefficient of variation (CV)7.0550758
Kurtosis104.74076
Mean0.0051035166
Median Absolute Deviation (MAD)3.15 × 10-6
Skewness9.5446683
Sum3.4754948
Variance0.0012964102
MonotonicityNot monotonic
2023-04-07T13:35:01.757794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 264
38.8%
1.64 × 10-67
 
1.0%
0.00179 5
 
0.7%
1.76 × 10-64
 
0.6%
0.00138 4
 
0.6%
0.00144 4
 
0.6%
6.16 × 10-63
 
0.4%
0.166 3
 
0.4%
1.19 × 10-53
 
0.4%
2.02 × 10-53
 
0.4%
Other values (230) 381
55.9%
ValueCountFrequency (%)
0 264
38.8%
1 × 10-62
 
0.3%
1.03 × 10-61
 
0.1%
1.04 × 10-62
 
0.3%
1.07 × 10-61
 
0.1%
1.08 × 10-62
 
0.3%
1.1 × 10-61
 
0.1%
1.2 × 10-62
 
0.3%
1.21 × 10-62
 
0.3%
1.23 × 10-61
 
0.1%
ValueCountFrequency (%)
0.488 1
 
0.1%
0.465 1
 
0.1%
0.348 1
 
0.1%
0.218 1
 
0.1%
0.184 2
0.3%
0.179 3
0.4%
0.166 3
0.4%
0.113 2
0.3%
0.0337 3
0.4%
0.00868 3
0.4%

En Vivo
Real number (ℝ)

Distinct231
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14138429
Minimum0.0335
Maximum0.594
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-04-07T13:35:02.238596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0335
5-th percentile0.073
Q10.0977
median0.114
Q30.148
95-th percentile0.333
Maximum0.594
Range0.5605
Interquartile range (IQR)0.0503

Descriptive statistics

Standard deviation0.077198274
Coefficient of variation (CV)0.54601735
Kurtosis4.3648978
Mean0.14138429
Median Absolute Deviation (MAD)0.0222
Skewness2.0084677
Sum96.2827
Variance0.0059595734
MonotonicityNot monotonic
2023-04-07T13:35:02.742466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.101 20
 
2.9%
0.113 20
 
2.9%
0.106 17
 
2.5%
0.114 14
 
2.1%
0.115 14
 
2.1%
0.121 14
 
2.1%
0.102 13
 
1.9%
0.108 13
 
1.9%
0.117 13
 
1.9%
0.111 12
 
1.8%
Other values (221) 531
78.0%
ValueCountFrequency (%)
0.0335 1
 
0.1%
0.0357 2
0.3%
0.0391 1
 
0.1%
0.0398 2
0.3%
0.0419 2
0.3%
0.0437 1
 
0.1%
0.0477 1
 
0.1%
0.0566 1
 
0.1%
0.0576 2
0.3%
0.0594 3
0.4%
ValueCountFrequency (%)
0.594 1
0.1%
0.499 1
0.1%
0.497 1
0.1%
0.483 1
0.1%
0.382 1
0.1%
0.37 2
0.3%
0.363 1
0.1%
0.36 2
0.3%
0.359 2
0.3%
0.356 1
0.1%

Sonoridad
Real number (ℝ)

Distinct400
Distinct (%)58.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.3329883
Minimum-15.48
Maximum-2.098
Zeros0
Zeros (%)0.0%
Negative681
Negative (%)100.0%
Memory size10.6 KiB
2023-04-07T13:35:03.222504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-15.48
5-th percentile-12.411
Q1-9.014
median-6.937
Q3-5.295
95-th percentile-3.648
Maximum-2.098
Range13.382
Interquartile range (IQR)3.719

Descriptive statistics

Standard deviation2.7765125
Coefficient of variation (CV)-0.37863316
Kurtosis-0.073132323
Mean-7.3329883
Median Absolute Deviation (MAD)1.912
Skewness-0.63123679
Sum-4993.765
Variance7.7090219
MonotonicityNot monotonic
2023-04-07T13:35:03.947554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9.195 5
 
0.7%
-7.039 5
 
0.7%
-4.991 5
 
0.7%
-8.193 5
 
0.7%
-10.813 4
 
0.6%
-8.768 4
 
0.6%
-6.112 4
 
0.6%
-5.572 4
 
0.6%
-5.414 4
 
0.6%
-5.421 4
 
0.6%
Other values (390) 637
93.5%
ValueCountFrequency (%)
-15.48 1
 
0.1%
-15.434 1
 
0.1%
-15.418 1
 
0.1%
-15.412 1
 
0.1%
-15.065 3
0.4%
-15.01 1
 
0.1%
-14.919 1
 
0.1%
-14.899 1
 
0.1%
-14.889 1
 
0.1%
-14.875 1
 
0.1%
ValueCountFrequency (%)
-2.098 2
0.3%
-2.627 2
0.3%
-2.641 2
0.3%
-2.881 2
0.3%
-2.934 1
0.1%
-2.94 2
0.3%
-2.976 2
0.3%
-3.002 2
0.3%
-3.057 1
0.1%
-3.079 1
0.1%

Letra
Real number (ℝ)

Distinct279
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.052882232
Minimum0.0239
Maximum0.519
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-04-07T13:35:04.340760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0239
5-th percentile0.026
Q10.0302
median0.0363
Q30.0537
95-th percentile0.165
Maximum0.519
Range0.4951
Interquartile range (IQR)0.0235

Descriptive statistics

Standard deviation0.049465025
Coefficient of variation (CV)0.93538082
Kurtosis24.398808
Mean0.052882232
Median Absolute Deviation (MAD)0.0082
Skewness4.2564289
Sum36.0128
Variance0.0024467887
MonotonicityNot monotonic
2023-04-07T13:35:04.690498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0264 14
 
2.1%
0.0308 10
 
1.5%
0.0273 8
 
1.2%
0.0275 8
 
1.2%
0.0323 8
 
1.2%
0.031 8
 
1.2%
0.0347 8
 
1.2%
0.0328 7
 
1.0%
0.0337 7
 
1.0%
0.0324 7
 
1.0%
Other values (269) 596
87.5%
ValueCountFrequency (%)
0.0239 2
 
0.3%
0.0243 5
0.7%
0.0245 3
0.4%
0.0246 2
 
0.3%
0.025 2
 
0.3%
0.0251 2
 
0.3%
0.0253 1
 
0.1%
0.0254 2
 
0.3%
0.0255 2
 
0.3%
0.0256 4
0.6%
ValueCountFrequency (%)
0.519 1
 
0.1%
0.402 1
 
0.1%
0.39 1
 
0.1%
0.364 1
 
0.1%
0.361 1
 
0.1%
0.249 2
0.3%
0.246 1
 
0.1%
0.245 3
0.4%
0.239 1
 
0.1%
0.204 1
 
0.1%

Tempo
Real number (ℝ)

Distinct419
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.66027
Minimum68.534
Maximum207.476
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-04-07T13:35:05.072459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum68.534
5-th percentile79.846
Q196.962
median119.966
Q3143.95
95-th percentile171.319
Maximum207.476
Range138.942
Interquartile range (IQR)46.988

Descriptive statistics

Standard deviation29.261573
Coefficient of variation (CV)0.23855788
Kurtosis-0.42309208
Mean122.66027
Median Absolute Deviation (MAD)23.863
Skewness0.42069587
Sum83531.642
Variance856.23966
MonotonicityNot monotonic
2023-04-07T13:35:05.407125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96.103 5
 
0.7%
109.993 4
 
0.6%
130.033 4
 
0.6%
103.981 4
 
0.6%
96.969 4
 
0.6%
159.965 3
 
0.4%
110.107 3
 
0.4%
85.984 3
 
0.4%
129.86 3
 
0.4%
116.992 3
 
0.4%
Other values (409) 645
94.7%
ValueCountFrequency (%)
68.534 1
 
0.1%
70.008 1
 
0.1%
71.981 2
0.3%
73.942 2
0.3%
73.975 1
 
0.1%
74.9 2
0.3%
74.952 3
0.4%
74.957 1
 
0.1%
75.602 3
0.4%
75.938 1
 
0.1%
ValueCountFrequency (%)
207.476 1
0.1%
204.125 1
0.1%
204.12 1
0.1%
203.959 2
0.3%
200.391 1
0.1%
200.056 1
0.1%
200.017 2
0.3%
199.997 2
0.3%
185.972 2
0.3%
185.262 1
0.1%

Valencia
Real number (ℝ)

Distinct312
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40921689
Minimum0.0374
Maximum0.943
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-04-07T13:35:05.784414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0374
5-th percentile0.113
Q10.248
median0.402
Q30.535
95-th percentile0.767
Maximum0.943
Range0.9056
Interquartile range (IQR)0.287

Descriptive statistics

Standard deviation0.19766889
Coefficient of variation (CV)0.48304188
Kurtosis-0.21739351
Mean0.40921689
Median Absolute Deviation (MAD)0.139
Skewness0.44559511
Sum278.6767
Variance0.039072992
MonotonicityNot monotonic
2023-04-07T13:35:06.397451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.211 8
 
1.2%
0.545 7
 
1.0%
0.287 7
 
1.0%
0.208 7
 
1.0%
0.472 7
 
1.0%
0.535 6
 
0.9%
0.374 6
 
0.9%
0.32 6
 
0.9%
0.467 6
 
0.9%
0.351 6
 
0.9%
Other values (302) 615
90.3%
ValueCountFrequency (%)
0.0374 1
0.1%
0.0379 1
0.1%
0.0382 1
0.1%
0.0384 1
0.1%
0.0499 1
0.1%
0.0656 1
0.1%
0.068 1
0.1%
0.0828 2
0.3%
0.0851 1
0.1%
0.0858 2
0.3%
ValueCountFrequency (%)
0.943 4
0.6%
0.942 3
0.4%
0.928 3
0.4%
0.92 3
0.4%
0.919 2
0.3%
0.865 1
 
0.1%
0.84 2
0.3%
0.831 1
 
0.1%
0.827 1
 
0.1%
0.825 2
0.3%

Popularidad
Real number (ℝ)

Distinct81
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.132159
Minimum4
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-04-07T13:35:06.855110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile17
Q138
median52
Q362
95-th percentile74
Maximum91
Range87
Interquartile range (IQR)24

Descriptive statistics

Standard deviation18.097534
Coefficient of variation (CV)0.36834397
Kurtosis-0.47976515
Mean49.132159
Median Absolute Deviation (MAD)12
Skewness-0.4843998
Sum33459
Variance327.52074
MonotonicityDecreasing
2023-04-07T13:35:07.176893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61 23
 
3.4%
62 23
 
3.4%
64 19
 
2.8%
59 19
 
2.8%
56 18
 
2.6%
57 18
 
2.6%
48 17
 
2.5%
63 16
 
2.3%
49 15
 
2.2%
40 15
 
2.2%
Other values (71) 498
73.1%
ValueCountFrequency (%)
4 1
 
0.1%
5 5
0.7%
6 3
0.4%
7 3
0.4%
8 1
 
0.1%
9 3
0.4%
10 2
 
0.3%
11 2
 
0.3%
12 7
1.0%
14 3
0.4%
ValueCountFrequency (%)
91 1
 
0.1%
85 1
 
0.1%
83 1
 
0.1%
82 3
 
0.4%
81 1
 
0.1%
80 1
 
0.1%
79 8
1.2%
78 2
 
0.3%
77 3
 
0.4%
76 3
 
0.4%

Interactions

2023-04-07T13:34:52.893314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:22.290746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:25.438501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:28.592721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:32.593024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:35.639494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:38.473059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:41.411327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:44.892667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:48.809827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:53.206669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:22.775881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:25.726759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:28.908955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:32.921922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:35.908209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:38.774411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:41.793395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:45.276609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:49.182122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:53.512682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:23.092929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:26.124538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:29.209155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:33.361085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:36.208203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:39.079934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:42.097701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:45.729104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:49.638438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:53.865397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:23.394175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:26.468110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:29.513110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:33.674778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:36.501541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:39.394694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:42.378396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:46.143455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:50.087403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:54.164699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:23.696214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:26.759485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:29.828661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:33.968259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:36.775924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:39.688626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:42.664386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:46.558486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:50.459513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:54.428905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:23.999206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:27.086719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:30.109368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:34.238617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:37.094335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:39.980980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:43.143390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:46.927943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:50.999303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:54.726680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:24.326566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:27.392790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:30.461115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:34.543005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:37.393417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:40.274007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:43.458967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:47.328783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:51.360527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:55.018645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:24.609140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:27.706949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:31.088403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:34.809469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:37.667437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:40.560006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:43.800739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:47.654746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:51.748847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:55.350972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:24.892267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:27.989701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:31.571086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:35.109793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:37.943433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:40.854854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:44.113328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:48.036090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:52.080687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:55.631370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:25.158730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:28.297301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:32.137043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:35.360056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:38.217004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:41.136008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:44.523153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:48.428496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-07T13:34:52.477178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-04-07T13:35:07.489390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
AcusticaBailabilidaEnergíaInstrumentalidadEn VivoSonoridadLetraTempoValenciaPopularidadAlbum
Acustica1.000-0.130-0.6820.050-0.061-0.707-0.006-0.039-0.2500.0220.274
Bailabilida-0.1301.0000.081-0.009-0.018-0.0140.215-0.1460.3330.0500.190
Energía-0.6820.0811.000-0.0060.1410.7530.2150.1730.537-0.0520.189
Instrumentalidad0.050-0.009-0.0061.000-0.145-0.3160.3180.0130.0240.0510.101
En Vivo-0.061-0.0180.141-0.1451.0000.1910.0020.084-0.044-0.0430.150
Sonoridad-0.707-0.0140.753-0.3160.1911.000-0.1600.1520.320-0.0980.332
Letra-0.0060.2150.2150.3180.002-0.1601.0000.2370.1880.2230.179
Tempo-0.039-0.1460.1730.0130.0840.1520.2371.0000.072-0.0030.135
Valencia-0.2500.3330.5370.024-0.0440.3200.1880.0721.000-0.0080.198
Popularidad0.0220.050-0.0520.051-0.043-0.0980.223-0.003-0.0081.0000.423
Album0.2740.1900.1890.1010.1500.3320.1790.1350.1980.4231.000

Missing values

2023-04-07T13:34:56.025335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-07T13:34:56.540162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

NombreAlbumAcusticaBailabilidaEnergíaInstrumentalidadEn VivoSonoridadLetraTempoValenciaPopularidad
42Anti-HeroMidnights0.1300.6370.6430.0000020.1420-6.5710.051997.0080.533091
40Lavender HazeMidnights0.2580.7330.4360.0005730.1570-10.4890.080096.9850.097685
401Don’t Blame Mereputation0.1060.6150.5340.0000180.0607-6.7190.0386135.9170.193083
45Midnight RainMidnights0.6900.6430.3630.0000520.1150-11.7380.0767139.8650.230082
334cardiganfolklore0.5370.6130.5810.0003450.2500-8.5880.0424130.0330.551082
670Blank Space1989 (Deluxe)0.1030.7600.7030.0000000.0913-5.4120.054095.9970.570082
367LoverLover0.4920.3590.5430.0000160.1180-7.5820.091968.5340.453081
366Cruel SummerLover0.1170.5520.7020.0000210.1050-5.7070.1570169.9940.564080
43Snow On The Beach (feat. Lana Del Rey)Midnights0.6900.6630.3190.0009930.1170-13.4810.0375109.9570.193079
44You're On Your Own, KidMidnights0.4010.6960.3960.0000050.1250-10.2890.0656120.0410.380079
NombreAlbumAcusticaBailabilidaEnergíaInstrumentalidadEn VivoSonoridadLetraTempoValenciaPopularidad
654Wonderland1989 (Deluxe Edition)0.045500.4250.6990.0000460.1740-5.4490.0481183.8800.20007
645All You Had To Do Was Stay1989 (Deluxe Edition)0.001960.6020.7360.0000460.1050-5.7780.033896.9690.47106
646Shake It Off1989 (Deluxe Edition)0.056100.6470.7850.0000000.1480-5.4140.1650160.0150.94306
647I Wish You Would1989 (Deluxe Edition)0.016000.6480.8980.0000990.1090-5.9630.0513118.0200.50206
641Welcome To New York1989 (Deluxe Edition)0.038000.7930.6340.0000020.3040-4.8070.0324117.0240.61505
649Wildest Dreams1989 (Deluxe Edition)0.070200.5540.6660.0059300.1060-7.4140.0747140.0560.47205
650How You Get The Girl1989 (Deluxe Edition)0.004610.7640.6600.0047700.0915-6.1360.0494119.9880.52405
652I Know Places1989 (Deluxe Edition)0.231000.5960.7630.0000000.2000-4.9900.0661159.9580.50705
653Clean1989 (Deluxe Edition)0.241000.8100.3790.0000000.1130-7.7710.0349103.9810.22005
651This Love1989 (Deluxe Edition)0.635000.4750.4590.0000000.1010-8.7680.0333143.9880.08284

Duplicate rows

Most frequently occurring

NombreAlbumAcusticaBailabilidaEnergíaInstrumentalidadEn VivoSonoridadLetraTempoValenciaPopularidad# duplicates
0Cold As YouTaylor Swift0.2170.4180.4820.00.123-5.7690.0266175.5580.261512